Title |
SAMSA2: a standalone metatranscriptome analysis pipeline
|
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Published in |
BMC Bioinformatics, May 2018
|
DOI | 10.1186/s12859-018-2189-z |
Pubmed ID | |
Authors |
Samuel T. Westreich, Michelle L. Treiber, David A. Mills, Ian Korf, Danielle G. Lemay |
Abstract |
Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms. SAMSA2 is an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for standalone use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution. SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the needs of any experiment. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 15 | 21% |
United Kingdom | 4 | 6% |
India | 3 | 4% |
Germany | 3 | 4% |
China | 3 | 4% |
Australia | 2 | 3% |
Netherlands | 2 | 3% |
Canada | 2 | 3% |
Switzerland | 2 | 3% |
Other | 12 | 17% |
Unknown | 22 | 31% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 38 | 54% |
Members of the public | 31 | 44% |
Practitioners (doctors, other healthcare professionals) | 1 | 1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 347 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 71 | 20% |
Researcher | 64 | 18% |
Student > Master | 45 | 13% |
Student > Doctoral Student | 24 | 7% |
Student > Bachelor | 22 | 6% |
Other | 42 | 12% |
Unknown | 79 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 89 | 26% |
Biochemistry, Genetics and Molecular Biology | 66 | 19% |
Environmental Science | 29 | 8% |
Immunology and Microbiology | 28 | 8% |
Computer Science | 12 | 3% |
Other | 33 | 10% |
Unknown | 90 | 26% |